KULLM: Learning to Construct Korean Instruction-Following Large Language Models

Seungjun Lee, Yoonna Jang, Jeongwook Kim, Taemin Lee, Heuiseok Lim

http://doi.org/10.5626/JOK.2024.51.9.817

The emergence of Large Language Models (LLMs) has revolutionized the research paradigm in natural language processing. While instruction-tuning techniques have been pivotal in enhancing LLM performance, the majority of current research has focused predominantly on English. This study addresses the need for multilingual approaches by presenting a method for developing and evaluating Korean instruction-following models. We fine-tuned LLM models using Korean instruction datasets and conducted a comprehensive performance analysis using various dataset combinations. The resulting Korean instruction-following model is made available as an open-source resource, contributing to the advancement of Korean LLM research. Our work aims to bridge the language gap in LLM development and promote more inclusive AI technologies.